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NEW QUESTION 44
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

• A. Build a knowledge-based filtering model
• B. Build a collaborative-based filtering model
• C. Build a classification model
• D. Build a regression model using the features as predictors

Answer: B

Explanation:
Reference:
https://developers.google.com/machine-learning/recommendation/collaborative/basics
https://cloud.google.com/architecture/recommendations-using-machine-learning-on-compute-engine#filtering_the_data

NEW QUESTION 45
You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

• A. Use the tf.distribute.Strategy API and run a distributed training job.
• B. Enable early stopping in your Vertex AI Training job.
• C. Replace the NVIDIA P100 GPU with a v3-32 TPU in the training job.
• D. Increase the instance memory to 512 GB and increase the batch size.

Answer: B

NEW QUESTION 46
A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter.
Which machine learning approach should be used to solve this problem?

• A. Principal component analysis (PCA)
• B. Random Cut Forest (RCF)
• C. Logistic regression
• D. Linear regression

Answer: B

NEW QUESTION 47
You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory dat a. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?

• A. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
• B. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.
• C. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data
• D. Use the TFX ModelValidator tools to specify performance metrics for production readiness

Answer: D

Explanation:
https://www.tensorflow.org/tfx/guide/evaluator

NEW QUESTION 48
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